Intelligent prediction of RBC demand in trauma patients using decision tree methods
Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classific...
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| Published in: | Military medical research Vol. 8; no. 1; pp. 33 - 12 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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BioMed Central
24.05.2021
Springer Nature B.V BMC |
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| ISSN: | 2054-9369, 2095-7467, 2054-9369 |
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| Abstract | Background
The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.
Methods
A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).
Results
For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756,
P
< 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853,
P
< 0.05). Haematocrit (Hct) is an important prediction parameter.
Conclusions
The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. |
|---|---|
| AbstractList | The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.
A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).
For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter.
The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). Results For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. Conclusions The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). Results For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. Conclusions The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. Abstract Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). Results For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. Conclusions The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.BACKGROUNDThe vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).METHODSA total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter.RESULTSFor non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter.The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.CONCLUSIONSThe classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment. |
| ArticleNumber | 33 |
| Author | Feng, Yan-Nan Liu, Jun-Ting Sun, Xiao-Lin Yu, Yang Wang, De-Qing Xu, Zhen-Hua |
| Author_xml | – sequence: 1 givenname: Yan-Nan surname: Feng fullname: Feng, Yan-Nan organization: Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital – sequence: 2 givenname: Zhen-Hua surname: Xu fullname: Xu, Zhen-Hua organization: Beijing Hexing Chuanglian Health Technology Co., Ltd – sequence: 3 givenname: Jun-Ting surname: Liu fullname: Liu, Jun-Ting organization: Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital – sequence: 4 givenname: Xiao-Lin surname: Sun fullname: Sun, Xiao-Lin organization: Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital – sequence: 5 givenname: De-Qing surname: Wang fullname: Wang, De-Qing email: deqingw@vip.sina.com organization: Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital – sequence: 6 givenname: Yang orcidid: 0000-0002-9390-1080 surname: Yu fullname: Yu, Yang email: yuyangpla301@163.com organization: Department of Transfusion Medicine, The First Medical Center of Chinese PLA General Hospital |
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| Keywords | Transfusion Intelligent prediction Mathematical model Non-invasive parameters Decision tree Invasive parameters Trauma |
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The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience... The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma... Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience... Abstract Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’... |
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| SubjectTerms | Adult Area Under Curve Artificial intelligence Blood pressure Blood Transfusion - methods Blood Transfusion - statistics & numerical data Blood transfusions China Classification Decision Support Techniques Decision tree Decision Trees Emergency Medicine Erythrocytes Female Forecasting - methods Hemorrhagic shock Hospitals Humans Intelligent prediction Invasive parameters Laboratories Logistic Models Male Mathematical model Medical prognosis Medicine Medicine & Public Health Middle Aged Mortality Natural language processing Non-invasive parameters Patients Physicians ROC Curve Trauma Trauma care Variables Vital signs Wounds and Injuries - physiopathology Wounds and Injuries - therapy |
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| Title | Intelligent prediction of RBC demand in trauma patients using decision tree methods |
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